Traditional cardiovascular risk assessment entails investigator‐defined exposure levels and individual risk markers in multivariable analysis. We sought to determine whether an alternative unbiased ...

Closed Loop Fracturing at scale enables unprecedented efficiencies, and performance optimization driven by live data interpolation and preconfigured decision trees. WILLOW PARK, Texas, Aug. 18, 2025 ...

Abstract: Understanding the underlying structure of medical data is essential for developing robust and reliable classification models. Supervised learning, which relies on predefined classes, may ...

Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V. Email: stefan [dot] stiller [at] zalf [dot] de, stillsen [at] gmail [dot] com This repository contains the code for the study ...

In this study, we employ a spatial unsupervised classification technique to analyze the spatio-temporal variability of Sea Surface Temperature (SST) in the tropical African zone. The methodology we ...

Abstract: The proposed article aims to apply and compare supervised and unsupervised classification methods on Sentinel-2 imagery, employing the Normalized Difference Water Index (NDWI) to detect the ...

Tesla just added full Robotaxi functionality in the latest app update—quietly prepping for launch next month. There is the need for Tesla to launch Supervised and unsupervised ridesharing in June in ...

Background: Free-text comments in patient-reported outcome measures (PROMs) data provide insights into health-related quality of life (HRQoL). However, these comments are typically analysed using ...

Supervised learning relies on historical, labeled data to train algorithms for specific outcomes. This approach is widely used in the financial sector, where reliable past data can guide future ...

Objective: This study aims to develop an unsupervised automated method for detecting high-frequency oscillations (HFOs) in intracranial electroencephalogram (iEEG) signals, addressing the limitations ...